Abstract

The rapid expansion of technical knowledge in engineering, manufacturing, and industrial domains presents significant challenges in knowledge capture, organization, and retrieval. Large Language Models (LLMs), such as GPT-based architectures, offer advanced natural language understanding and generation capabilities that can transform technical knowledge management. This paper explores the application of LLMs for automating documentation, extracting insights from unstructured technical data, and providing intelligent query-based knowledge retrieval. The proposed framework integrates domain-specific corpora, structured databases, and real-time user feedback to continuously refine model accuracy and relevance. Case studies in aerospace, manufacturing, and power systems demonstrate enhanced efficiency in technical documentation, reduced information retrieval times, and improved decision-making through contextualized knowledge suggestions. Moreover, the system supports collaborative knowledge sharing and maintenance of institutional expertise across teams. Findings highlight that LLMs not only streamline technical knowledge workflows but also enable proactive maintenance, innovation, and training in industrial environments. The integration of LLMs into knowledge management systems represents a scalable, AI-driven solution to address the growing complexity and volume of technical information in modern enterprises.